This document describes the re-estimation of the VE Multimodal Module (VE-MM) using the 2017 National Household Travel Survey (2017 NHTS). The original version of the VE-MM was estimated using the 2009 NHTS.
The models described in this memo include:
Data used to estimate the VE-MM models came from several sources. The 2017 NHTS confidential data were obtained from ORNL via FHWA. The data were provided with Census Block Group which allowed data from the EPA Smart Location Database, version 3 (SLD) to be connected to the household. Additional spatial information at the metropolitan area level were added to the data including transit revenue miles from the National Transit Database and freeway lane miles from FHWA published data.
The AADVMT model is estimated using the total annual miles driven by vehicles owned by the NHTS households divided by 365, instead of estimating a daily VMT model based on the reported travel during the survey day. The distributions of those travel amounts differs in the data.
In particular there are a lot of “zero days” in the survey data for the survey day. There are also more very long days in the survey data with more than 200 miles of travel reported. With the averaging over the year that takes place by using AADVMT, a lot of the day to day variability within a household’s travel is removed. The final chart compares the none-zero day distribution and other than the extremes at the low end and high end of the distribution, the distributions are reasonably comparable.
Figure 1: Distribution of AADVMT per Household in the 2017 NHTS
Figure 2: Distribution of Survey Day DVMT per Household in the 2017 NHTS
Figure 3: Comparison between AADVMT and Survey Day DVMT per Household in the 2017 NHTS
The households with the highest 1% of AADVMT were excluded from the estimation dataset. The exclusion of outliers has the effect of reducing the average AADVMT per household. The table below shows the impact on the average AADVMT per household for metro and non-metro households. The charts shows the change in the binned distribution of households for metro and non-metro households.
All of the analysis in the remainder of this document is based on the 2017 NHTS data with outliers removed, consistent with the set of households used for estimation.
| Outlier Status | Metro HH | Non-Metro HH | Metro Avg. AADVMT | Non-Metro Avg. AADVMT |
|---|---|---|---|---|
| Included | 71740 | 56646 | 47.54 | 61.01 |
| Outlier | 617 | 693 | 359.50 | 293.96 |
| Total | 72357 | 57339 | 50.85 | 64.96 |
Figure 4: Impacts of Outlier Filtering on the AADVMT per Household in the 2017 NHTS
One of the important explanatory variables for household AADVMT is the population density of the neighborhood in which the household resides. The 2017 NHTS data were linked to the SLD and the D1B variable, which contains the Census Block Group population density in units of persons per acre. The ranges of density varies significantly in the sample of NHTS households. As is expected, most non-metro households live in low density areas while the range of densities for metropolitan households is generally higher but covers several orders of magnitude. The following box plot, plotted on a log scale shows the distribution of households by population density.
Figure 5: Distribution of Households in the 2017 NHTS by Population Density
The following charts show the relationship between AADVMT in low, medium, and high density areas, split by metro and non-metro households in low and medium density areas, and showing just metropolitan households in the high density chart. The trends in the charts show a clear relationship between density and AADVMT: as density increases, AADVMT tends to fall. There are some anomalous values, particular for non-metropolitan households, due to small sample sizes.
Figure 6: 2017 NHTS AADVMT by Density for Households in Low Density Neighborhoods
Figure 7: 2017 NHTS AADVMT by Density for Households in Medium Density Neighborhoods
Figure 8: 2017 NHTS AADVMT by Density for Households in High Density Neighborhoods
Household income has a positive impact on the amount of travel a household makes, with higher income households traveling more. The first chart shows the distribution of income levels amongst the households in the 2017 NHTS sample.
Just over 4,000 (3%) of the households in the sample were missing a response to the household income question. For the purpose of this analysis they have been recoded with an income in the median income category, which has a midpoint of $62,500.
The second chart shows the relationship between AADVMT and household income and shows shows a clear postiive trend for both metropolitan and non-metropolitan households.
Figure 9: Distribution of Households in the 2017 NHTS by Household Income
Figure 10: 2017 NHTS AADVMT by Household Income
Household size has a positive impact on the amount of travel a household makes, with larger households traveling more. The first chart shows the distribution of household size amongst the households in the 2017 NHTS sample. The second charts shows the relationship between AADVMT and household size and shows shows a clear postiive trend for both metropolitan and non-metropolitan households.
Figure 11: Distribution of Households in the 2017 NHTS by Household Size
Figure 12: 2017 NHTS AADVMT by Household Size
The household’s life cycle stage, i.e., whether the household is a single person, a couple without children, a couple with children, or older “empty nesters” influences the amount of travel a household makes. Multi person household make more travel and adding children to the household causes a further moderate increase in the amount of travel. The first chart shows the distribution of life cycle stage amongst the households in the 2017 NHTS sample. The second charts shows the relationship between AADVMT and household life cycle stage for both metropolitan and non-metropolitan households.
Figure 13: Distribution of Households in the 2017 NHTS by Household Life Cycle Stage
Figure 14: 2017 NHTS AADVMT by Household Life Cycle Stage
The number of workers in the household positively impacts the amount of travel a household makes. With each additional worker in the household, the household make more travel. The first chart shows the distribution of number of workers per households amongst the households in the 2017 NHTS sample. The second charts shows the relationship between AADVMT and number of workers per household for both metropolitan and non-metropolitan households.
Figure 15: Distribution of Households in the 2017 NHTS by Number of Workers in the Household
Figure 16: 2017 NHTS AADVMT by Number of Workers in the Household
The number of drivers in the household positively impacts the amount of travel a household makes. With each additional driver in the household, the household make more travel. The first chart shows the distribution of number of drivers per households amongst the households in the 2017 NHTS sample. The second charts shows the relationship between AADVMT and number of drivers per household for both metropolitan and non-metropolitan households.
Figure 17: Distribution of Households in the 2017 NHTS by Number of Drivers in the Household
Figure 18: 2017 NHTS AADVMT by Number of Drivers in the Household
The 2017 NHTS sample includes some households in Oregon. Oregon was not an “add on” state and therefore has sample from just the all-US sample, a total of 385 households. Average AADVMt per household in metro areas is slightly lower in Oregon than the average for the rest of the US, and is significantly lower for non-metro households.
| State | Metro HH | Non-Metro HH | Metro Avg. AADVMT | Non-Metro Avg. AADVMT |
|---|---|---|---|---|
| Oregon | 236 | 148 | 47.32 | 55.97 |
| Other State | 71504 | 56498 | 47.54 | 61.09 |
The income distribution for the Oregon households is reasonably similar to that for the other states, with a notable difference being a low number of high income households in non-metro areas. The positive effect of income of AADVMT is clear, although it is negligible across the upper categories in metro areas. The data for non-metro areas is less reliable for the high income categories due to small sample sizes.
Figure 19: Oregon and Other State Household Income Distribution
Figure 20: Oregon and Other State AADVMT by Household Income
The following table shows the AADVMT model estimated using the 2017 NHTS for Metro areas and Non-Metro area.
| Dependent variable: | ||
| AADVMT | ||
| NONMETRO | METRO | |
| (1) | (2) | |
| Drivers | 0.929*** (0.012) | 0.995*** (0.008) |
| HhSize | 0.105*** (0.010) | |
| Workers | 0.234*** (0.009) | 0.151*** (0.008) |
| CENSUS_RNE | -0.069*** (0.019) | -0.085*** (0.016) |
| CENSUS_RS | 0.095*** (0.014) | 0.012 (0.014) |
| CENSUS_RW | -0.212*** (0.017) | -0.119*** (0.015) |
| FwyLaneMiPC | 49.622*** (17.024) | |
| LogIncomeK | 0.351*** (0.007) | 0.190*** (0.006) |
| Age0to14 | -0.003 (0.012) | 0.090*** (0.009) |
| Age65Plus | -0.072*** (0.011) | -0.096*** (0.010) |
| log1p(VehPerDriver) | 4.105*** (0.025) | 4.262*** (0.021) |
| LifeCycleCouple w/o children | -0.033 (0.021) | -0.059*** (0.016) |
| LifeCycleEmpty Nester | -0.267*** (0.026) | -0.456*** (0.020) |
| LifeCycleSingle | -0.211*** (0.028) | -0.413*** (0.018) |
| D1B | -0.019*** (0.003) | -0.002*** (0.0002) |
| D2A_EPHHM | -0.298*** (0.029) | |
| D1B:D2A_EPHHM | 0.023*** (0.005) | |
| D2A_WRKEMP | -0.0002 (0.0002) | |
| D3bpo4 | -0.001*** (0.0001) | |
| TranRevMiPC:D4c | -0.056*** (0.004) | |
| Constant | -0.802*** (0.045) | -0.320*** (0.031) |
| Observations | 56,551 | 71,740 |
| R2 | 0.654 | 0.700 |
| Adjusted R2 | 0.654 | 0.700 |
| Residual Std. Error | 1.182 (df = 56534) | 1.384 (df = 71722) |
| F Statistic | 6,671.994*** (df = 16; 56534) | 9,861.924*** (df = 17; 71722) |
| Note: | p<0.1; p<0.05; p<0.01 | |
The following tables compare the AADVMT models estimated using the 2009 and 2017 NHTS.
This first comparison is between 2009 and 2017 AADVMT Models for Metro areas.
| VarName | NHTS2009 | NHTS2017 | Ratio |
|---|---|---|---|
| (Intercept) | -1.333 | -0.320 | 0.240 |
| Age0to14 | 0.107 | 0.090 | 0.840 |
| Age65Plus | -0.075 | -0.096 | 1.286 |
| CENSUS_RNE | -0.109 | -0.085 | 0.777 |
| CENSUS_RS | 0.051 | 0.012 | 0.227 |
| CENSUS_RW | -0.092 | -0.119 | 1.289 |
| D1B | -0.003 | -0.002 | 0.690 |
| D2A_WRKEMP | 0.000 | 0.000 | 0.914 |
| D3bpo4 | -0.001 | -0.001 | 0.892 |
| Drivers | 0.705 | 0.995 | 1.412 |
| FwyLaneMiPC | 101.341 | 49.622 | 0.490 |
| LifeCycleCouple w/o children | -0.036 | -0.059 | 1.611 |
| LifeCycleEmpty Nester | -0.256 | -0.456 | 1.778 |
| LifeCycleSingle | -0.234 | -0.413 | 1.767 |
| LogIncome | 0.268 | 0.000 | 0.000 |
| LogIncomeK | 0.000 | 0.190 | Inf |
| TranRevMiPC:D4c | -0.020 | -0.056 | 2.821 |
| Workers | 0.186 | 0.151 | 0.811 |
| log1p(VehPerDriver) | 1.794 | 4.262 | 2.376 |
This second comparison is between 2009 and 2017 AADVMT Models for Non-Metro areas.
| VarName | NHTS2009 | NHTS2017 | Ratio |
|---|---|---|---|
| (Intercept) | -1.416 | -0.802 | 0.566 |
| Age0to14 | 0.102 | -0.003 | -0.031 |
| Age65Plus | -0.077 | -0.072 | 0.932 |
| CENSUS_RNE | -0.112 | -0.069 | 0.617 |
| CENSUS_RS | 0.058 | 0.095 | 1.632 |
| CENSUS_RW | -0.176 | -0.212 | 1.206 |
| D1B | -0.008 | -0.019 | 2.325 |
| D1B:D2A_EPHHM | -0.027 | 0.023 | -0.867 |
| D2A_EPHHM | -0.084 | -0.298 | 3.524 |
| Drivers | 0.744 | 0.929 | 1.249 |
| HhSize | 0.017 | 0.105 | 6.231 |
| LifeCycleCouple w/o children | -0.013 | -0.033 | 2.421 |
| LifeCycleEmpty Nester | -0.208 | -0.267 | 1.281 |
| LifeCycleSingle | -0.216 | -0.211 | 0.975 |
| LogIncome | 0.288 | 0.000 | 0.000 |
| LogIncomeK | 0.000 | 0.351 | Inf |
| Workers | 0.177 | 0.234 | 1.322 |
| log1p(VehPerDriver) | 1.852 | 4.105 | 2.216 |
One aspect of the NHTS AADVMT data that is hard to capture, even with a power transform adjusted linear model, is the variability in the data due to unobserved household travel characteristics. Some households just travel more or less than other similar households with similar income, transportation access, and neighborhood characteristics.
In order to capture this dispersion, a random variable factor has been drawn for each household to factor the predicted AADVMT to simulate household to household variation. This random variable is drawn from a left skewed normal distribution to allow for some households to have lower AADVMT, to overall achieve a slight increase in AADVMT to account for systematic under prediction of the mean AADVMT in the sample, and to produce a longer tail of households with a higher AADVMT.
Figure 21: Skewed Normal Distribution
| Metro or Non-Metro | Weighted HH | NHTS2017 AADVMT/HH | Model AADVMT/HH | Model Sim AADVMT/HH | Ratio Model/NHTS2017 | Ratio Model Sim/NHTS2017 |
|---|---|---|---|---|---|---|
| metro | 81842.28 | 47.54 | 43.17 | 47.61 | 0.91 | 1 |
| non_metro | 46121.44 | 61.04 | 56.58 | 61.06 | 0.93 | 1 |
Figure 22: Scatterplot of Model Prediction vs. 2017 NHTS Data
(#fig:scatter-aadvmt-model-prediction_rnd)Scatterplot of Model Prediction (Simulated) vs. 2017 NHTS Data
Figure 23: Households by DVMT Bins, Model Prediction vs. 2017 NHTS Data
Figure 24: Difference in Households in DVMT Bins (Model Prediction - 2017 NHTS Data)
Figure 25: Difference in Households in DVMT Bins (Model Prediction (Simulated) - 2017 NHTS Data)
Figure 26: Difference in Households in DVMT Bins (Model Prediction and Simulated - 2017 NHTS Data)
| HH Income | Metro WgtHH | Non-Metro WgtHH | Metro Obs AADVMT/HH | Non-Metro Obs AADVMT/HH | Metro Pred AADVMT/HH | Non-Metro Pred AADVMT/HH | Metro Sim AADVMT/HH | Non-Metro Sim AADVMT/HH | Metro Ratio Pred/Obs | Non-Metro Ratio Pred/Obs | Metro Ratio Sim/Obs | Non-Metro Ratio Sim/Obs |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 5000 | 6086.08 | 3265.04 | 18.53 | 20.41 | 15.01 | 16.83 | 16.50 | 18.54 | 0.81 | 0.82 | 0.89 | 0.91 |
| 12500 | 4376.62 | 3136.07 | 21.29 | 29.89 | 19.51 | 25.93 | 21.59 | 28.02 | 0.92 | 0.87 | 1.01 | 0.94 |
| 19999 | 7380.65 | 4786.65 | 29.94 | 40.09 | 26.98 | 35.28 | 29.69 | 37.92 | 0.90 | 0.88 | 0.99 | 0.95 |
| 30000 | 7568.41 | 4899.85 | 37.60 | 48.02 | 33.40 | 43.04 | 37.00 | 46.02 | 0.89 | 0.90 | 0.98 | 0.96 |
| 42499 | 9554.17 | 5807.06 | 44.58 | 57.59 | 39.06 | 53.08 | 42.90 | 57.24 | 0.88 | 0.92 | 0.96 | 0.99 |
| 62500 | 15152.85 | 9288.25 | 48.06 | 65.62 | 43.50 | 61.75 | 48.03 | 66.72 | 0.91 | 0.94 | 1.00 | 1.02 |
| 87500 | 9827.42 | 5408.12 | 56.70 | 79.17 | 51.13 | 74.24 | 56.23 | 79.49 | 0.90 | 0.94 | 0.99 | 1.00 |
| 112500 | 7638.21 | 4000.40 | 64.35 | 88.30 | 59.21 | 79.73 | 65.41 | 86.15 | 0.92 | 0.90 | 1.02 | 0.98 |
| 137500 | 4486.90 | 2099.04 | 68.08 | 88.35 | 62.07 | 85.63 | 68.27 | 92.82 | 0.91 | 0.97 | 1.00 | 1.05 |
| 174999 | 4683.85 | 1748.72 | 68.91 | 91.53 | 64.81 | 87.45 | 71.83 | 95.68 | 0.94 | 0.96 | 1.04 | 1.05 |
| 249999 | 5087.13 | 1682.24 | 68.33 | 93.31 | 65.93 | 94.16 | 72.71 | 102.33 | 0.96 | 1.01 | 1.06 | 1.10 |
Figure 27: Households by Income Group, Model Prediction vs. 2017 NHTS Data
Figure 28: Scatterplot of Model Prediction vs. 2017 NHTS Data by Income
| D1B Group | Metro WgtHH | Non-Metro WgtHH | Metro Obs AADVMT/HH | Non-Metro Obs AADVMT/HH | Metro Pred AADVMT/HH | Non-Metro Pred AADVMT/HH | Metro Sim AADVMT/HH | Non-Metro Sim AADVMT/HH | Metro Ratio Pred/Obs | Non-Metro Ratio Pred/Obs | Metro Ratio Sim/Obs | Non-Metro Ratio Sim/Obs |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 50657.88 | 44851.81 | 53.35 | 61.42 | 48.59 | 56.92 | 53.60 | 61.44 | 0.91 | 0.93 | 1.00 | 1.00 |
| 10 | 17457.51 | 1103.06 | 44.74 | 48.65 | 40.84 | 45.20 | 45.07 | 48.04 | 0.91 | 0.93 | 1.01 | 0.99 |
| 20 | 4807.56 | 109.48 | 39.94 | 48.37 | 35.23 | 39.52 | 38.80 | 44.01 | 0.88 | 0.82 | 0.97 | 0.91 |
| 30 | 2442.79 | 33.11 | 33.59 | 36.09 | 28.41 | 52.89 | 31.83 | 66.00 | 0.85 | 1.47 | 0.95 | 1.83 |
| 40 | 1380.94 | 23.97 | 30.18 | 13.51 | 27.10 | 24.34 | 29.28 | 26.51 | 0.90 | 1.80 | 0.97 | 1.96 |
| 50 | 985.24 | 0.00 | 23.74 | 0.00 | 22.24 | 0.00 | 24.27 | 0.00 | 0.94 | 0.00 | 1.02 | 0.00 |
| 60 | 649.40 | 0.00 | 22.28 | 0.00 | 22.41 | 0.00 | 24.29 | 0.00 | 1.01 | 0.00 | 1.09 | 0.00 |
| 70 | 445.27 | 0.00 | 20.13 | 0.00 | 17.65 | 0.00 | 19.43 | 0.00 | 0.88 | 0.00 | 0.97 | 0.00 |
| 80 | 386.24 | 0.00 | 20.74 | 0.00 | 18.31 | 0.00 | 19.49 | 0.00 | 0.88 | 0.00 | 0.94 | 0.00 |
| 90 | 344.47 | 0.00 | 17.75 | 0.00 | 17.85 | 0.00 | 18.56 | 0.00 | 1.01 | 0.00 | 1.05 | 0.00 |
| 100 | 1539.17 | 0.00 | 14.92 | 0.00 | 12.76 | 0.00 | 14.26 | 0.00 | 0.86 | 0.00 | 0.96 | 0.00 |
| 200 | 745.81 | 0.00 | 9.43 | 0.00 | 6.90 | 0.00 | 7.51 | 0.00 | 0.73 | 0.00 | 0.80 | 0.00 |
Figure 29: Households by Density, Model Prediction vs. 2017 NHTS Data
Figure 30: Scatterplot of Model Prediction vs. 2017 NHTS Data by Density
| HH Size | Metro WgtHH | Non-Metro WgtHH | Metro Obs AADVMT/HH | Non-Metro Obs AADVMT/HH | Metro Pred AADVMT/HH | Non-Metro Pred AADVMT/HH | Metro Sim AADVMT/HH | Non-Metro Sim AADVMT/HH | Metro Ratio Pred/Obs | Non-Metro Ratio Pred/Obs | Metro Ratio Sim/Obs | Non-Metro Ratio Sim/Obs |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 24826.85 | 11185.76 | 22.38 | 28.57 | 20.53 | 26.82 | 22.69 | 28.89 | 0.92 | 0.94 | 1.01 | 1.01 |
| 2 | 26637.43 | 16940.21 | 47.44 | 59.50 | 41.94 | 53.59 | 46.23 | 57.72 | 0.88 | 0.90 | 0.97 | 0.97 |
| 3 | 12614.19 | 7323.02 | 61.62 | 78.82 | 55.40 | 71.32 | 60.89 | 76.42 | 0.90 | 0.90 | 0.99 | 0.97 |
| 4 | 11550.85 | 6568.33 | 70.45 | 84.68 | 65.36 | 80.01 | 72.34 | 87.58 | 0.93 | 0.94 | 1.03 | 1.03 |
| 5 | 4232.36 | 2540.60 | 75.39 | 85.51 | 71.52 | 84.55 | 79.14 | 92.21 | 0.95 | 0.99 | 1.05 | 1.08 |
| 6 | 1980.60 | 1563.52 | 81.19 | 87.61 | 75.69 | 88.87 | 82.39 | 93.49 | 0.93 | 1.01 | 1.01 | 1.07 |
Figure 31: Households by Size, Model Prediction vs. 2017 NHTS Data
Figure 32: Scatterplot of Model Prediction vs. 2017 NHTS Data by Household Size
| Workers | Metro WgtHH | Non-Metro WgtHH | Metro Obs AADVMT/HH | Non-Metro Obs AADVMT/HH | Metro Pred AADVMT/HH | Non-Metro Pred AADVMT/HH | Metro Sim AADVMT/HH | Non-Metro Sim AADVMT/HH | Metro Ratio Pred/Obs | Non-Metro Ratio Pred/Obs | Metro Ratio Sim/Obs | Non-Metro Ratio Sim/Obs |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 20288.99 | 14255.02 | 23.26 | 34.24 | 21.50 | 31.10 | 23.69 | 33.72 | 0.92 | 0.91 | 1.02 | 0.99 |
| 1 | 32134.24 | 15903.13 | 41.30 | 56.57 | 36.52 | 50.97 | 40.28 | 54.87 | 0.88 | 0.90 | 0.98 | 0.97 |
| 2 | 24189.03 | 13063.12 | 66.00 | 83.73 | 58.99 | 77.08 | 64.87 | 83.02 | 0.89 | 0.92 | 0.98 | 0.99 |
| 3 | 4088.31 | 2383.94 | 90.39 | 111.44 | 88.02 | 113.20 | 97.34 | 122.64 | 0.97 | 1.02 | 1.08 | 1.10 |
| 4 | 956.45 | 458.84 | 106.01 | 130.78 | 114.64 | 148.19 | 130.25 | 161.17 | 1.08 | 1.13 | 1.23 | 1.23 |
| 5 | 185.26 | 57.39 | 129.07 | 141.05 | 147.17 | 184.89 | 162.25 | 210.10 | 1.14 | 1.31 | 1.26 | 1.49 |
Figure 33: Households by Workers, Model Prediction vs. 2017 NHTS Data
Figure 34: Scatterplot of Model Prediction vs. 2017 NHTS Data by Number of Workers
| Drivers | Metro WgtHH | Non-Metro WgtHH | Metro Obs AADVMT/HH | Non-Metro Obs AADVMT/HH | Metro Pred AADVMT/HH | Non-Metro Pred AADVMT/HH | Metro Sim AADVMT/HH | Non-Metro Sim AADVMT/HH | Metro Ratio Pred/Obs | Non-Metro Ratio Pred/Obs | Metro Ratio Sim/Obs | Non-Metro Ratio Sim/Obs |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 6384.30 | 1775.83 | 0.16 | 0.79 | 0.01 | 0.09 | 0.01 | 0.10 | 0.08 | 0.12 | 0.09 | 0.12 |
| 1 | 29508.16 | 13948.61 | 28.85 | 33.85 | 26.90 | 32.31 | 29.76 | 34.86 | 0.93 | 0.95 | 1.03 | 1.03 |
| 2 | 35915.65 | 23736.80 | 58.82 | 69.71 | 51.45 | 61.90 | 56.56 | 67.00 | 0.87 | 0.89 | 0.96 | 0.96 |
| 3 | 7266.06 | 5100.45 | 84.46 | 97.00 | 78.05 | 92.47 | 86.28 | 98.84 | 0.92 | 0.95 | 1.02 | 1.02 |
| 4 | 2296.87 | 1276.29 | 108.70 | 120.81 | 110.80 | 133.56 | 124.26 | 146.45 | 1.02 | 1.11 | 1.14 | 1.21 |
| 5 | 471.24 | 283.45 | 132.09 | 135.04 | 148.30 | 166.05 | 158.42 | 171.51 | 1.12 | 1.23 | 1.20 | 1.27 |
Figure 35: Households by Drivers, Model Prediction vs. 2017 NHTS Data
Figure 36: Scatterplot of Model Prediction vs. 2017 NHTS Data by Number of Drivers
| Vehicles | Metro WgtHH | Non-Metro WgtHH | Metro Obs AADVMT/HH | Non-Metro Obs AADVMT/HH | Metro Pred AADVMT/HH | Non-Metro Pred AADVMT/HH | Metro Sim AADVMT/HH | Non-Metro Sim AADVMT/HH | Metro Ratio Pred/Obs | Non-Metro Ratio Pred/Obs | Metro Ratio Sim/Obs | Non-Metro Ratio Sim/Obs |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 9461.80 | 2451.54 | 0.00 | 0.00 | 0.81 | 1.44 | 0.88 | 1.49 | Inf | Inf | Inf | Inf |
| 1 | 30836.06 | 13477.84 | 29.24 | 30.42 | 25.27 | 25.88 | 27.91 | 27.96 | 0.86 | 0.85 | 0.95 | 0.92 |
| 2 | 27673.26 | 17077.66 | 61.01 | 63.28 | 54.00 | 57.67 | 59.51 | 62.23 | 0.89 | 0.91 | 0.98 | 0.98 |
| 3 | 9594.74 | 8686.58 | 84.56 | 90.47 | 79.80 | 84.72 | 87.88 | 91.68 | 0.94 | 0.94 | 1.04 | 1.01 |
| 4 | 3188.06 | 3068.35 | 109.21 | 114.26 | 108.00 | 114.60 | 118.12 | 123.66 | 0.99 | 1.00 | 1.08 | 1.08 |
| 5 | 900.21 | 941.75 | 127.92 | 136.72 | 128.68 | 134.01 | 147.27 | 141.45 | 1.01 | 0.98 | 1.15 | 1.03 |
| 6 | 136.87 | 298.75 | 144.54 | 139.28 | 141.93 | 144.62 | 156.63 | 158.95 | 0.98 | 1.04 | 1.08 | 1.14 |
| 7 | 21.04 | 73.31 | 129.45 | 129.46 | 125.35 | 115.32 | 135.78 | 132.20 | 0.97 | 0.89 | 1.05 | 1.02 |
| 8 | 7.87 | 18.90 | 176.98 | 154.07 | 161.11 | 152.48 | 176.05 | 160.04 | 0.91 | 0.99 | 0.99 | 1.04 |
| 9 | 15.02 | 20.31 | 106.11 | 206.69 | 160.39 | 148.29 | 159.40 | 160.72 | 1.51 | 0.72 | 1.50 | 0.78 |
| 10 | 7.18 | 1.16 | 10.89 | 124.34 | 63.58 | 102.14 | 68.12 | 96.06 | 5.84 | 0.82 | 6.26 | 0.77 |
| 11 | 0.05 | 4.59 | 151.46 | 209.79 | 109.63 | 113.56 | 105.98 | 96.27 | 0.72 | 0.54 | 0.70 | 0.46 |
| 12 | 0.10 | 0.71 | 111.86 | 210.32 | 74.69 | 118.83 | 101.45 | 128.47 | 0.67 | 0.56 | 0.91 | 0.61 |
Figure 37: Households by Drivers, Model Prediction vs. 2017 NHTS Data
Figure 38: Scatterplot of Model Prediction vs. 2017 NHTS Data by Number of Vehicles
The following tables and chart compare the performance of the 2009 and 2017 AADVMT Models by applying the 2009 model to the 2017 NHTS households.
| Metro or Non-Metro | Num HH | NHTS2017 AADVMT/HH | Model (2017) AADVMT/HH | Model (2009) AADVMT/HH | Ratio Model (2017)/NHTS2017 | Ratio Model (2009)/NHTS2017 |
|---|---|---|---|---|---|---|
| metro | 71740 | 47.54 | 47.61 | 42.37 | 1 | 0.89 |
| non_metro | 56538 | 61.04 | 61.06 | 53.65 | 1 | 0.88 |
Figure 39: Scatterplot of 2009 Model Prediction vs. 2017 NHTS Data
Figure 40: Scatterplot of 2009 Model Prediction vs. 2017 Model Prediction
The initial estimation of the model shows that, with the addition of the random variable factor drawn from the skewed normal distribution, the 2017 AADVMT model is able to replicate the mean AADVMT in the NHTS 2017, capture variation across the categories of explanatory variables, and capture household to household variability reasonably well.
However, the approach to estimating the model with reported AADVMT as the dependent variable is fundamentally different from what the model is tasked with predicting in the VE application. In the application, the AADVMT model is used to estimate household Dvmt based on the assumption that the amount of travel a household makes is not yet constrained by cost. Then a second model is applied in VE to reduce the Dvmt for households that exceed a certain household budget threshold for the maximum proportion of their income that they can spend on transportation costs.
Reported AADVMT in the survey is analogous with the budget adjusted Dvmt, i.e., it is the final actual travel that a household makes once any constraints such as spending have been considered in real life. Therefore, to be consistent with the model application approach and the use of the budget model, the dependent variable should be an estimated of household AADVMT that is unconstrained by costs.
In order to estimate a model using this approach, the model reported earlier in this document was applied in the Oregon implementation of VE-State and the proportional reduction in Dvmt between the application of the AADVMT model and the second of two iterations through the budget model was calculated for each household income category. This factor was then used to estimate a non-household budget constrained AADVMT for each household in 2017 NHTS. The AADVMT model was then re-estimated using these higher values of AADVMT.
The tables and charts below show the factors calculated from applying the model initially, the resulting changes in AADVMT by household income groups, the parameters of the re-estimated AADVMT model, and the results of applying the re-estimated model back to the 2017 NHTS data.
The appliations include the original VMT model estimated using the 2001 NHTS, the version of the AADVMT model estimated using the 2009 NHTS, and the initial version of the 2017 NHTS model.
| HH Income | Dvmt | Dvmt (1st budget iteration) | Dvmt (2nd budget iteration) | Model Version | Percent Increase in Dvmt Required |
|---|---|---|---|---|---|
| 5000 | 28.35 | 15.08 | 14.65 | MMNew | 1.94 |
| 12000 | 35.28 | 26.54 | 26.01 | MMNew | 1.36 |
| 20000 | 39.07 | 31.38 | 30.88 | MMNew | 1.27 |
| 30000 | 42.70 | 36.13 | 35.65 | MMNew | 1.20 |
| 42000 | 46.58 | 40.93 | 40.47 | MMNew | 1.15 |
| 62000 | 51.38 | 46.43 | 46.03 | MMNew | 1.12 |
| 87000 | 56.00 | 51.79 | 51.39 | MMNew | 1.09 |
| 112000 | 59.88 | 56.29 | 55.91 | MMNew | 1.07 |
| 137000 | 63.29 | 60.02 | 59.68 | MMNew | 1.06 |
| 175000 | 67.28 | 64.40 | 64.08 | MMNew | 1.05 |
| 250000 | 68.74 | 66.48 | 66.26 | MMNew | 1.04 |
| 5000 | 21.33 | 14.76 | 14.39 | MMOld | 1.48 |
| 12000 | 33.02 | 27.25 | 26.74 | MMOld | 1.23 |
| 20000 | 38.54 | 33.11 | 32.58 | MMOld | 1.18 |
| 30000 | 44.14 | 39.15 | 38.62 | MMOld | 1.14 |
| 42000 | 49.78 | 45.16 | 44.63 | MMOld | 1.12 |
| 62000 | 56.37 | 52.20 | 51.70 | MMOld | 1.09 |
| 87000 | 62.90 | 59.18 | 58.72 | MMOld | 1.07 |
| 112000 | 68.18 | 64.87 | 64.43 | MMOld | 1.06 |
| 137000 | 72.92 | 69.80 | 69.38 | MMOld | 1.05 |
| 175000 | 78.44 | 75.56 | 75.18 | MMOld | 1.04 |
| 250000 | 81.38 | 79.07 | 78.80 | MMOld | 1.03 |
| 5000 | 25.55 | 16.19 | 15.78 | Orig | 1.62 |
| 12000 | 36.68 | 29.05 | 28.47 | Orig | 1.29 |
| 20000 | 41.76 | 34.78 | 34.18 | Orig | 1.22 |
| 30000 | 46.88 | 40.62 | 40.03 | Orig | 1.17 |
| 42000 | 52.15 | 46.48 | 45.89 | Orig | 1.14 |
| 62000 | 58.38 | 53.34 | 52.79 | Orig | 1.11 |
| 87000 | 64.52 | 60.15 | 59.64 | Orig | 1.08 |
| 112000 | 69.61 | 65.76 | 65.29 | Orig | 1.07 |
| 137000 | 74.16 | 70.65 | 70.18 | Orig | 1.06 |
| 175000 | 79.41 | 76.40 | 75.96 | Orig | 1.05 |
| 250000 | 86.10 | 83.16 | 82.86 | Orig | 1.04 |
The chart below shows the effect of appling the adjustments calculated from the 2017 NHTS model to the 2017 NHTS. The lower income categories are effected much more significantly than the higher income categories, flattening the impact of income. This is of course the expected outcome as lower income households are far more likely to hit a budget threshold even where they are assumed to spend higher overall proportions of their income on transportation,
Figure 41: Surveyed and Budget Adjusted AADVMT from the 2017 NHTS
The table below shows the re-estimated parameters of the AADVMT model, estimated using the budget adjusted AADVMT as the dependent variable.
| Dependent variable: | ||
| AADVMT | ||
| NONMETRO | METRO | |
| (1) | (2) | |
| Drivers | 1.017*** (0.012) | 1.077*** (0.009) |
| HhSize | 0.106*** (0.011) | |
| Workers | 0.242*** (0.010) | 0.145*** (0.009) |
| CENSUS_RNE | -0.066*** (0.020) | -0.090*** (0.017) |
| CENSUS_RS | 0.099*** (0.015) | 0.011 (0.015) |
| CENSUS_RW | -0.217*** (0.018) | -0.126*** (0.016) |
| FwyLaneMiPC | 46.162** (18.093) | |
| LogIncomeK | 0.140*** (0.008) | 0.017*** (0.006) |
| Age0to14 | 0.003 (0.013) | 0.094*** (0.010) |
| Age65Plus | -0.072*** (0.011) | -0.107*** (0.011) |
| log1p(VehPerDriver) | 4.565*** (0.026) | 4.698*** (0.022) |
| LifeCycleCouple w/o children | -0.030 (0.023) | -0.062*** (0.016) |
| LifeCycleEmpty Nester | -0.291*** (0.028) | -0.494*** (0.022) |
| LifeCycleSingle | -0.229*** (0.030) | -0.440*** (0.019) |
| D1B | -0.018*** (0.003) | -0.002*** (0.0002) |
| D2A_EPHHM | -0.308*** (0.031) | |
| D1B:D2A_EPHHM | 0.022*** (0.006) | |
| D2A_WRKEMP | -0.0002 (0.0002) | |
| D3bpo4 | -0.001*** (0.0001) | |
| TranRevMiPC:D4c | -0.047*** (0.005) | |
| Constant | -0.148*** (0.048) | 0.238*** (0.033) |
| Observations | 56,566 | 71,725 |
| R2 | 0.630 | 0.689 |
| Adjusted R2 | 0.630 | 0.689 |
| Residual Std. Error | 1.266 (df = 56549) | 1.471 (df = 71707) |
| F Statistic | 6,025.424*** (df = 16; 56549) | 9,353.162*** (df = 17; 71707) |
| Note: | p<0.1; p<0.05; p<0.01 | |
When the model is applied back to the NHTS 2017 households, the results below show that, once the random variable simulation is complete, the results match the estimate of unconstrained household AADVMT. Reversing the factoring process results in an estimate of budget constrained household AADVMT that matches the original report AADVMT in the 2017 NHTS. It is likely that some calibration of the adjustment factors for each income categort that were used to factor the NHTS AADVMT for the budget adjusted estimation may be necessary. This is dependent on the response of the budget model during application testing.
| Metro or Non-Metro | Weighted HH | NHTS2017 AADVMT/HH | NHTS2017 Unconstrained AADVMT/HH | Model AADVMT/HH | Model Sim AADVMT/HH | Model Budget Adj AADVMT/HH | Ratio Model/ Uncon NHTS2017 | Ratio Model Sim/ Uncon NHTS2017 | Ratio Model Budget/ NHTS 2017 |
|---|---|---|---|---|---|---|---|---|---|
| metro | 81842.28 | 47.54 | 53.64 | 48.54 | 53.66 | 47.77 | 0.90 | 1 | 1.01 |
| non_metro | 46121.44 | 61.04 | 69.33 | 64.13 | 69.08 | 61.09 | 0.93 | 1 | 1.00 |